Huggingface Inference Server. Run open-source AI models locally or connect to cloud models like
Run open-source AI models locally or connect to cloud models like GPT, Claude and others. 2 Klein image generation models on JarvisLabs GPUs. There are several services you can connect to: Deploy any AI model from the Hugging Face Hub in minutes. Inference is run by Hugging Face in a dedicated, fully managed infrastructure on a cloud provider of your choice. 1 day ago · It covers how the SDK integrates with PyTorch, TensorFlow, scikit-learn, ONNX Runtime, Triton Inference Server, MLflow, and HuggingFace frameworks. Both methods accept an optional namespace Transformer models can be efficiently deployed using libraries such as vLLM, Text Generation Inference (TGI), and others. Key Features In-Browser Inference: WebLLM is a high-performance, in-browser language model inference engine that leverages WebGPU for hardware acceleration, enabling powerful LLM operations directly within web browsers without server-side processing. Contribute to MarshallMcfly/llama-cpp development by creating an account on GitHub. Covers text-to-image, image editing, and multi-reference composition with the 4B distilled model in 4 inference steps. Use Hugging Face's hosted Inference API to execute AI tasks remotely on Hugging Face servers. Oct 2, 2025 · Choose between Hugging Face vs Replicate for model hosting, inference APIs, and community features based on your project requirements. huggingface) HuggingFace is one of the largest communities of AI builders and open source in the world. Once connected, your assistant can search and explore Hub resources and use community tools, all from within your editor, chat or CLI. This is particularly useful when you use the non-streaming invoke method but still want to stream the entire application, including intermediate results from the chat model. After launching the server, you can use the Messages API /v1/chat/completions route and make a POST request to get results from the server. There are several services you can connect to: We’re on a journey to advance and democratize artificial intelligence through open source and open science. There are several services you can connect to: Just to share a quick update. Overview Authentication Environment variables Hugging Face Hub API CLI Downloading files Mixins & serialization methods Inference Types Inference Client Inference Endpoints MCP Client HfFileSystem Utilities Discussions and Pull Requests Cache-system reference Repo Cards and Repo Card Data Space runtime Collections TensorBoard logger Webhooks server Serialization Strict dataclasses OAuth Jobs We’re on a journey to advance and democratize artificial intelligence through open source and open science. The Inference API can be accessed via usual HTTP requests with your favorite programming language, but the huggingface_hub library has a client wrapper to access the Inference API programmatically. Web server inference A web server is a system that waits for requests and serves them as they come in. Alternatively, you can use list_inference_endpoints () to retrieve a list of all Inference Endpoints. yaml file using batchSize and maxBatchDelay parameters. Official inference repo for FLUX. The document explains which packages depend on which frameworks, version constraints, and how frameworks are used across training and inference workflows. Run FLUX. There are several services you can connect to: The Inference API provides fast inference for your hosted models. Hundreds of users have tried it to create their very own AI Aug 5, 2025 · We’re on a journey to advance and democratize artificial intelligence through open source and open science. By default, the server applies generation_config. The Hugging Face MCP (Model Context Protocol) Server connects your MCP‑compatible AI assistant (for example VS Code, Cursor, Zed, or Claude Desktop) directly to the Hugging Face Hub. Jan 9, 2026 · This guide provides step-by-step instructions for running distributed LLM inference using llama. 1-70B-Instruct - Powerful reasoning mistralai/Mistral-7B-Instruct-v0. There are several services you can connect to: Inference is the process of using a trained model to make predictions on new data. 2 | Red Hat Documentation Home A modern web-based dashboard for managing vLLM inference servers. Some popular models are supported even if they exceed 10GB. Dec 12, 2023 · With a keen focus on simplifying the intricate backend processes, olympipe transforms the ordeal of setting up an NLP inference server from a daunting task into a streamlined, manageable Jan 26, 2025 · However, did you know that Hugging Face also offers an inference API, allowing you to run models on their servers, similar to how the OpenAI API works? In this article, I will demonstrate how to use the following methods to access Hugging Face models in the cloud: The huggingface_hub library provides a unified interface to run inference across multiple services for models hosted on the Hugging Face Hub: Inference Providers: a streamlined, unified access to hundreds of machine learning models, powered by our serverless inference partners. Run Inference on servers Inference is the process of using a trained model to make predictions on new data. Workloads in NVIDIA Run:ai Workload Templates Inference Templates Hugging Face Inference Templates This section explains how to create Hugging Face inference templates for reuse during workload submission. TGI enables high-performance text generation for the most popular open-source LLMs, including Llama, Falcon, StarCoder, BLOOM, GPT-NeoX, and T5. For this example, the ViT model available on HuggingFace is being used. With the same semantics as language models, spice can run private HuggingFace embedding models: 4 days ago · We have all been there. cpp, Ollama, vLLM, LiteLLM, or Text Generation Inference (TGI) by connecting the client to these local endpoints. 1-8B-Instruct - Fast and reliable meta-llama/Llama-3. [Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, HuggingFace, VLLM, NVIDIA NIM] - BerriAI/litellm LLM inference in C/C++. The discussion in this guide will focus on how a user can deploy almost any model from HuggingFace with the Triton Inference Server. An example of such an application is the AI Comic Factory – a Space that has proved incredibly popular. Deploy popular AI models in 1 click. The Inference API provides fast inference for your hosted models. A Colab notebook. It allows you to easily test and compare models hosted on Hugging Face, connect to different third-party Inference Providers, and even configure your own custom OpenAI-compatible endpoints. The output is stunning, the inference is fast, and you immediately start picturing how to integrate it into your Local QuickStart (Recommended - Truly Free) FriendliAI (Paid Inference) HuggingFace Inference API YouTube Tutorials Summary The Future of Efficient Agents Implications for the Industry Looking Ahead Conclusion: Is AgentCPM-Explore Right for You? Best Use Cases When to Consider Alternatives My Recommendation FAQ: Your AgentCPM-Explore Questions 17 hours ago · HuggingFace Inference (vision_agents. If you know the name, you can fetch it using get_inference_endpoint (), which returns an InferenceEndpoint object. Starting in 0. txt again and testing on another endpoint, the endpoint just worked anyway! So I’m not sure what changed. Both methods accept an optional namespace The Hugging Face MCP (Model Context Protocol) Server connects your MCP‑compatible AI assistant (for example Codex, Cursor, VS Code extensions, Zed, ChatGPT or Claude Desktop) directly to the Hugging Face Hub. To manage templates, see Workload Templates. The huggingface_hub library provides a unified interface to run inference across multiple services for models hosted on the Hugging Face Hub: Inference Providers: a streamlined, unified access to hundreds of machine learning models, powered by our serverless inference partners. 3 - Efficient French-developed model Deploy any model from HuggingFace: deploy any embedding, reranking, clip and sentence-transformer model from HuggingFace Fast inference backends: The inference server is built on top of PyTorch, optimum (ONNX/TensorRT) and CTranslate2, using FlashAttention to get the most out of your NVIDIA CUDA, AMD ROCM, CPU, AWS INF2 or APPLE MPS accelerator. Apr 14, 2025 · This application provides a user interface to interact with various large language models, leveraging the @huggingface/inference library. We also recommend using only fine-grained tokens for production usage. plugins. The impact, if leaked, will be reduced, and they can be shared among your organization without impacting your account. As this process can be compute-intensive, running on a dedicated server can be an interesting option. You can use this workflow as a reference and adapt it for your own models, container images, and hardware configurations. 17 hours ago · HuggingFace Inference (vision_agents. Comprehensive deployment instructions are available in the official Github repository. Jan 12, 2026 · Qwen downloads in December surpassed the combined total of the next eight most popular models on Hugging Face. The performance of LLM models can vary greatly depending on factors like input prompts, decoding strategies, hardware specifications, and server configurations. Mar 14, 2025 · AMD is excited to announce the integration of Google’s Gemma 3 models with AMD Instinct™ MI300X GPUs The official Python client for the Hugging Face Hub. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry. js. Supported models include: All models tagged as text-embeddings-inference on Huggingface Any Huggingface repository with the correct files to be loaded as a local embedding model. An awesome custom inference server. 3, developers can now run their favourite open-weight models directly in Vision Agents using our HuggingFace Inference package. These libraries are designed for production-grade user-facing services, and can scale to multiple servers and millions of concurrent users. - huggingface_hub/docs/source/en/guides/cli. 1-405B-Instruct model. Discover Hugging Face's gpt-oss-20b model, a smaller open-source AI with versatile applications and fine-tuning capabilities for developers and researchers. Go to Endpoints Catalog and in the Inference Server options, select vLLM. This guide will walk you through this approach. Limits: $0. Jan 8, 2025 · Setting up Triton Inference Server with vLLM backend # To perform inference with large language models using the Triton Inference Server and vLLM backend, follow these steps: Set up Triton Inference Server with vLLM backend: We are configuring a docker compose file that includes a Triton Inference Server container with the vLLM backend. Use the Transformers Python library to perform inference in a Python backend. 7-Flash supports inference frameworks including vLLM and SGLang. txt: transformers diffusers accelerate mediapipe After updating the endpoint, it started to work! But after removing them from requirements. Refer to Transformers as Backend for Inference Servers for usage examples. Note 🤖 HuggingFace Integration (Enhanced) Access any of the 800,000+ models on HuggingFace Hub via their Inference API with improved reliability: meta-llama/Llama-3. This way, you can invalidate one token without impacting your other usages. Inference is the process of using a trained model to make predictions on new data. Select the desired model and click Create Endpoint. 7 supports inference frameworks including vLLM and SGLang. There are many ways to consume Text Generation Inference (TGI) server in your applications. Ready integration with popular HuggingFace models verl is fast with: State-of-the-art throughput: By seamlessly integrating existing SOTA LLM training and inference frameworks, verl achieves high generation and training throughput. Deploy any AI model from the Hugging Face Hub in minutes. You can read more about batch inference in TorchServe here. Downloading a model from Hugging Face before running Red Hat AI Inference Server | Getting started | Red Hat Enterprise Linux AI | 3. This means the default values of certain sampling parameters can be overridden by those recommended by the model creator. For local deployment, GLM-4. Python SDK, Proxy Server (AI Gateway) to call 100+ LLM APIs in OpenAI (or native) format, with cost tracking, guardrails, loadbalancing and logging. HuggingFace is a popular source of many open source models. 10/month in credits Various open models across supported providers LangChain simplifies streaming from chat models by automatically enabling streaming mode in certain cases, even when you’re not explicitly calling the streaming methods. Instead of spending weeks configuring infrastructure, managing servers, and debugging deployment issues, you can focus on what matters most: your model and your users. The main advanatage of the Hugging Face MCP Server is that it provides a Inference is the process of using a trained model to make predictions on new data. Local endpoints: you can also run inference with local inference servers like llama. You find an incredible new AI model on Hugging Face. I added the following to the requirements. HuggingFace Inference Providers HuggingFace Serverless Inference limited to models smaller than 10GB. In LangGraph agents, for example, you can call Inference Benchmarker The best tool for benchmarking inference engines and LLM performance Benchmarking inference servers for text generation models presents unique challenges. Text Generation Inference (TGI) is a toolkit for deploying and serving Large Language Models (LLMs). This will display the current list of models with optimized preconfigured options. This chance opens up latest possibilities for running end-user applications using Hugging Face as a platform. I got it to work, but unsure exactly what may have caused the issue. This means you can use Pipeline as an inference engine on a web server, since you can use an iterator (similar to how you would iterate over a dataset) to handle each incoming request. Inference is the process of using a trained model to make predictions on new data. Inference Inference is the process of using a trained model to make predictions on new data. Check out our awesome list for a broader collection of gpt-oss resources and inference partners. In some cases, you might need to manage Inference Endpoints you created previously. May 24, 2023 · vLLM is a fast and easy-to-use library for LLM inference and serving. With the same semantics as language models, spice can run private HuggingFace embedding models: Supported models include: All models tagged as text-embeddings-inference on Huggingface Any Huggingface repository with the correct files to be loaded as a local embedding model. 17 hours ago · We’re on a journey to advance and democratize artificial intelligence through open source and open science. 1 day ago · For local deployment, GLM-4. There are several services you can connect to: Oct 11, 2020 · SUMMARY In this blog post, We examine Nvidia’s Triton Inference Server (formerly known as TensorRT Inference Server) which simplifies the deployment of AI models at scale in production. . With the same semantics as language models, spice can run private HuggingFace embedding models: We’re on a journey to advance and democratize artificial intelligence through open source and open science. Switch between model configurations, monitor GPU utilization, download models from HuggingFace, and control containers—all without Jan 11, 2026 · For LLM deployments, the endpoint typically runs the Text Generation Inference (TGI) container from HuggingFace—a critical component that deserves deeper explanation. json from the huggingface model repository if it exists. The huggingface_hub library provides an easy way to call a service that runs inference for hosted models. We’re on a journey to advance and democratize artificial intelligence through open source and open science. 2 models. Because this process can be compute-intensive, running on a dedicated or external service can be an interesting option. Inference Endpoints makes deploying AI models to production a smooth experience. A local machine. Contribute to black-forest-labs/flux2 development by creating an account on GitHub. Generate embeddings directly in Edge Functions using Transformers. The number of maxWorkers you deploy should be equal to or smaller than the number of cards you have in your server or container Batch inference can also be configured in the model-config. Jan is an open-source alternative to ChatGPT. This new approach builds on our previous Serverless Inference API, offering more models, improved performance, and greater reliability thanks to world-class providers. Oct 21, 2025 · Two inference modes: Run locally via HuggingFace Transformers, or deploy a vLLM server for production throughput Layout-aware output: Every text block, table, and image comes with bounding box coordinates 3 days ago · We recently announced Inference for PROs, our latest offering that makes larger models accessible to a broader audience. md at main · huggingface/huggingface_hub Tutorials Inference Tutorials Hugging Face Distributed Inference Deployment This tutorial demonstrates how to run a distributed inference workload using the Llama-3. cpp's RPC backend on multiple reComputer Jetson devices.